Evidence map›Paper›PMID 36351723›Full record

ArticleBMJ open2022

What should nurses do on post-stroke depression? A global systematic assessment of clinical practice guidelines.

Li Jiang, Linqing Cheng, Xingyu Li, Jie Yan

Abstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Li JiangSchool of Life Science and Technology, Jiangsu University Jingjiang College, Zhenjiang, Jiangsu, China nzyjiangli@163.com jdluocf@163.com.ORCID 0000-0002-4600-802X
Linqing ChengSchool of Life Science and Technology, Jiangsu University Jingjiang College, Zhenjiang, Jiangsu, China.
Xingyu LiSchool of Life Science and Technology, Jiangsu University Jingjiang College, Zhenjiang, Jiangsu, China.
Jie YanSchool of Life Science and Technology, Jiangsu University Jingjiang College, Zhenjiang, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesPost-stroke depression (PSD) is the most common mental disorder in post-stroke patients. Yet, the recommendations related to nursing in clinical practice guidelines (CPGs) have not been systematically sorted out. This study aimed to assess the methodological quality of current CPGs related to PSD and develop an algorithm using nursing process as a framework for nurses.

designA systematic assessment of CPGs.

interventionsA systematic search for relevant CPGs published between 2017 and 2022 was conducted. Appraisal of Guidelines for Research and Evaluation Ⅱ instrument was used to assess methodological quality. Recommendations related to nursing practice from high-quality CPGs were summarised and developed into an algorithm to provide reference for the standardised construction of nursing practice scheme.

results497 records were initially identified from database searches and other sources. Finally, 12 CPGs were included, of which 6 were rated as high quality. A total of 35 recommendations from the 6 highest-scoring CPGs were summarised and used to develop an algorithm.

conclusionsThis study indicated deficiencies and variability in current available CPGs. Based on six high-quality CPGs, we developed an algorithm to facilitate nurses' adherence to CPGs and contribute to evidence-based nursing. In the future, more nursing specialists should participate in the formulation of the CPGs to provide nursing insights.

Indexed as

DepressionStrokeAlgorithmsDatabases, FactualHumansadult psychiatrydepression & mood disordersstroke

Identifiers

PMID36351723
PMCPMC9644339

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.